Literature DB >> 27171505

A CMOS-compatible electronic synapse device based on Cu/SiO2/W programmable metallization cells.

Wenhao Chen1, Runchen Fang, Mehmet B Balaban, Weijie Yu, Yago Gonzalez-Velo, Hugh J Barnaby, Michael N Kozicki.   

Abstract

In this work, the resistance plasticity of Cu/SiO2/W programmable metallization cell devices is experimentally explored for the emulation of biological synapses. PMC devices were fabricated with foundry friendly materials using standard processes. The resistance can be continuously increased or decreased with both dc and voltage pulse programming. Impedance spectroscopy results indicate that the gradual change of resistance is attributable to the expansion or contraction of a Cu-rich layer within the device. Pulse programming experiments further show that the pulse amplitude plays a more important role in resistance change than pulse width, which is consistent with the proposed 'dual-layer' device model. The dense resistance-state distribution, 1 V operating voltage and inherent CMOS-compatibility suggests its potential application as electronic synapse in neuromorphic computing.

Entities:  

Year:  2016        PMID: 27171505     DOI: 10.1088/0957-4484/27/25/255202

Source DB:  PubMed          Journal:  Nanotechnology        ISSN: 0957-4484            Impact factor:   3.874


  3 in total

1.  Signal and noise extraction from analog memory elements for neuromorphic computing.

Authors:  N Gong; T Idé; S Kim; I Boybat; A Sebastian; V Narayanan; T Ando
Journal:  Nat Commun       Date:  2018-05-29       Impact factor: 14.919

2.  Efficient and self-adaptive in-situ learning in multilayer memristor neural networks.

Authors:  Can Li; Daniel Belkin; Yunning Li; Peng Yan; Miao Hu; Ning Ge; Hao Jiang; Eric Montgomery; Peng Lin; Zhongrui Wang; Wenhao Song; John Paul Strachan; Mark Barnell; Qing Wu; R Stanley Williams; J Joshua Yang; Qiangfei Xia
Journal:  Nat Commun       Date:  2018-06-19       Impact factor: 14.919

Review 3.  Progress of Materials and Devices for Neuromorphic Vision Sensors.

Authors:  Sung Woon Cho; Chanho Jo; Yong-Hoon Kim; Sung Kyu Park
Journal:  Nanomicro Lett       Date:  2022-10-15
  3 in total

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